metadata

tags:
  - stable-diffusion-xl
  - stable-diffusion-xl-diffusers
  - text-to-image
  - diffusers
  - lora
  - template:sd-lora
widget:
  - text: Cat dressed as boxer in the style of <s0><s1>
    output:
      url: image-0.png
  - text: Cat farmer in the style of <s0><s1>
    output:
      url: image-1.png
  - text: Cat scientist in the style of <s0><s1>
    output:
      url: image-2.png
  - text: Cat soldier in the style of <s0><s1>
    output:
      url: image-3.png
  - text: Cat politician in the style of <s0><s1>
    output:
      url: image-4.png
  - text: Cat fireman in the style of <s0><s1>
    output:
      url: image-5.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: in the style of <s0><s1>
license: openrail++

SDXL LoRA DreamBooth - saddad/geminicat

Prompt
Cat dressed as boxer in the style of <s0><s1>

Prompt
Cat farmer in the style of <s0><s1>

Prompt
Cat scientist in the style of <s0><s1>

Prompt
Cat soldier in the style of <s0><s1>

Prompt
Cat politician in the style of <s0><s1>

Prompt
Cat fireman in the style of <s0><s1>

Model description

These are saddad/geminicat LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

Download model

Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke

LoRA: download geminicat.safetensors here 💾.
- Place it on your models/Lora folder.
- On AUTOMATIC1111, load the LoRA by adding <lora:geminicat:1> to your prompt. On ComfyUI just load it as a regular LoRA.
Embeddings: download geminicat_emb.safetensors here 💾.
- Place it on it on your embeddings folder
- Use it by adding geminicat_emb to your prompt. For example, in the style of geminicat_emb (you need both the LoRA and the embeddings as they were trained together for this LoRA)

Use it with the 🧨 diffusers library

from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
        
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('saddad/geminicat', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='saddad/geminicat', filename='geminicat_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
        
image = pipeline('in the style of <s0><s1>').images[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

Trigger words

To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:

to trigger concept TOK → use <s0><s1> in your prompt

Details

All Files & versions.

The weights were trained using 🧨 diffusers Advanced Dreambooth Training Script.

LoRA for the text encoder was enabled. False.

Pivotal tuning was enabled: True.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.